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      <h1 class="site-logo" id="site-title">深入浅出PyTorch</h1>
      
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   第零章：前置知识
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     人工智能简史
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     模型评价指标
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    <a class="reference internal" href="../%E7%AC%AC%E9%9B%B6%E7%AB%A0/0.3%20%E5%B8%B8%E7%94%A8%E5%8C%85%E7%9A%84%E5%AD%A6%E4%B9%A0.html">
     常用包的学习
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    <a class="reference internal" href="../%E7%AC%AC%E9%9B%B6%E7%AB%A0/0.4%20Jupyter%E7%9B%B8%E5%85%B3%E6%93%8D%E4%BD%9C.html">
     Jupyter notebook/Lab 简述
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   第一章：PyTorch的简介和安装
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     1.1 PyTorch简介
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     1.2 PyTorch的安装
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     1.3 PyTorch相关资源
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   第二章：PyTorch基础知识
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     2.1 张量
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     2.2 自动求导
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     2.3 并行计算简介
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     AI硬件加速设备
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   第三章：PyTorch的主要组成模块
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     3.1 思考：完成深度学习的必要部分
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     3.2 基本配置
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     3.3 数据读入
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     3.4 模型构建
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     3.5 模型初始化
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     3.6 损失函数
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     3.7 训练和评估
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     3.8 可视化
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     3.9 PyTorch优化器
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   第四章：PyTorch基础实战
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     4.1 ResNet
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     基础实战——FashionMNIST时装分类
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   第五章：PyTorch模型定义
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     5.1 PyTorch模型定义的方式
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     5.2 利用模型块快速搭建复杂网络
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     5.3 PyTorch修改模型
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     5.4 PyTorch模型保存与读取
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   第六章：PyTorch进阶训练技巧
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     6.1 自定义损失函数
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     6.2 动态调整学习率
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     6.3 模型微调-torchvision
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     6.3 模型微调 - timm
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    <a class="reference internal" href="../%E7%AC%AC%E5%85%AD%E7%AB%A0/6.4%20%E5%8D%8A%E7%B2%BE%E5%BA%A6%E8%AE%AD%E7%BB%83.html">
     6.4 半精度训练
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    <a class="reference internal" href="../%E7%AC%AC%E5%85%AD%E7%AB%A0/6.5%20%E6%95%B0%E6%8D%AE%E5%A2%9E%E5%BC%BA-imgaug.html">
     6.5 数据增强-imgaug
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    <a class="reference internal" href="../%E7%AC%AC%E5%85%AD%E7%AB%A0/6.6%20%E4%BD%BF%E7%94%A8argparse%E8%BF%9B%E8%A1%8C%E8%B0%83%E5%8F%82.html">
     6.6 使用argparse进行调参
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   第七章：PyTorch可视化
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     7.1 可视化网络结构
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     7.2 CNN可视化
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     7.3 使用TensorBoard可视化训练过程
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     7.4 使用wandb可视化训练过程
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   第八章：PyTorch生态简介
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     8.1 本章简介
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   5.3.1 修改模型层
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                <h1>5.3 PyTorch修改模型</h1>
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  <section class="tex2jax_ignore mathjax_ignore" id="pytorch">
<h1>5.3 PyTorch修改模型<a class="headerlink" href="#pytorch" title="永久链接至标题">#</a></h1>
<p>除了自己构建PyTorch模型外，还有另一种应用场景：我们已经有一个现成的模型，但该模型中的部分结构不符合我们的要求，为了使用模型，我们需要对模型结构进行必要的修改。随着深度学习的发展和PyTorch越来越广泛的使用，有越来越多的开源模型可以供我们使用，很多时候我们也不必从头开始构建模型。因此，掌握如何修改PyTorch模型就显得尤为重要。</p>
<p>本节我们就来探索这一问题。经过本节的学习，你将收获：</p>
<ul class="simple">
<li><p>如何在已有模型的基础上：</p>
<ul>
<li><p>修改模型若干层</p></li>
<li><p>添加额外输入</p></li>
<li><p>添加额外输出</p></li>
</ul>
</li>
</ul>
<section id="id1">
<h2>5.3.1 修改模型层<a class="headerlink" href="#id1" title="永久链接至标题">#</a></h2>
<p>我们这里以PyTorch官方视觉库torchvision预定义好的模型ResNet50为例，探索如何修改模型的某一层或者某几层。我们先看看模型的定义是怎样的：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># 导入必要的package</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
<span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
<span class="n">net</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet50</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">net</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ResNet</span><span class="p">(</span>
  <span class="p">(</span><span class="n">conv1</span><span class="p">):</span> <span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
  <span class="p">(</span><span class="n">bn1</span><span class="p">):</span> <span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-05</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">track_running_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
  <span class="p">(</span><span class="n">relu</span><span class="p">):</span> <span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
  <span class="p">(</span><span class="n">maxpool</span><span class="p">):</span> <span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ceil_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
  <span class="p">(</span><span class="n">layer1</span><span class="p">):</span> <span class="n">Sequential</span><span class="p">(</span>
    <span class="p">(</span><span class="mi">0</span><span class="p">):</span> <span class="n">Bottleneck</span><span class="p">(</span>
      <span class="p">(</span><span class="n">conv1</span><span class="p">):</span> <span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
      <span class="p">(</span><span class="n">bn1</span><span class="p">):</span> <span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-05</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">track_running_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
      <span class="p">(</span><span class="n">conv2</span><span class="p">):</span> <span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
      <span class="p">(</span><span class="n">bn2</span><span class="p">):</span> <span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-05</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">track_running_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
      <span class="p">(</span><span class="n">conv3</span><span class="p">):</span> <span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
      <span class="p">(</span><span class="n">bn3</span><span class="p">):</span> <span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-05</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">track_running_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
      <span class="p">(</span><span class="n">relu</span><span class="p">):</span> <span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
      <span class="p">(</span><span class="n">downsample</span><span class="p">):</span> <span class="n">Sequential</span><span class="p">(</span>
        <span class="p">(</span><span class="mi">0</span><span class="p">):</span> <span class="n">Conv2d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
        <span class="p">(</span><span class="mi">1</span><span class="p">):</span> <span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-05</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">track_running_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
      <span class="p">)</span>
    <span class="p">)</span>
<span class="o">..............</span>
  <span class="p">(</span><span class="n">avgpool</span><span class="p">):</span> <span class="n">AdaptiveAvgPool2d</span><span class="p">(</span><span class="n">output_size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
  <span class="p">(</span><span class="n">fc</span><span class="p">):</span> <span class="n">Linear</span><span class="p">(</span><span class="n">in_features</span><span class="o">=</span><span class="mi">2048</span><span class="p">,</span> <span class="n">out_features</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="p">)</span>

</pre></div>
</div>
<p>这里模型结构是为了适配ImageNet预训练的权重，因此最后全连接层（fc）的输出节点数是1000。</p>
<p>假设我们要用这个resnet模型去做一个10分类的问题，就应该修改模型的fc层，将其输出节点数替换为10。另外，我们觉得一层全连接层可能太少了，想再加一层。可以做如下修改：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">classifier</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">OrderedDict</span><span class="p">([(</span><span class="s1">&#39;fc1&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">2048</span><span class="p">,</span> <span class="mi">128</span><span class="p">)),</span>
                          <span class="p">(</span><span class="s1">&#39;relu1&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()),</span> 
                          <span class="p">(</span><span class="s1">&#39;dropout1&#39;</span><span class="p">,</span><span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)),</span>
                          <span class="p">(</span><span class="s1">&#39;fc2&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span>
                          <span class="p">(</span><span class="s1">&#39;output&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Softmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
                          <span class="p">]))</span>
    
<span class="n">net</span><span class="o">.</span><span class="n">fc</span> <span class="o">=</span> <span class="n">classifier</span>
</pre></div>
</div>
<p>这里的操作相当于将模型（net）最后名称为“fc”的层替换成了名称为“classifier”的结构，该结构是我们自己定义的。这里使用了第一节介绍的Sequential+OrderedDict的模型定义方式。至此，我们就完成了模型的修改，现在的模型就可以去做10分类任务了。</p>
</section>
<section id="id2">
<h2>5.3.2 添加外部输入<a class="headerlink" href="#id2" title="永久链接至标题">#</a></h2>
<p>有时候在模型训练中，除了已有模型的输入之外，还需要输入额外的信息。比如在CNN网络中，我们除了输入图像，还需要同时输入图像对应的其他信息，这时候就需要在已有的CNN网络中添加额外的输入变量。基本思路是：将原模型添加输入位置前的部分作为一个整体，同时在forward中定义好原模型不变的部分、添加的输入和后续层之间的连接关系，从而完成模型的修改。</p>
<p>我们以torchvision的resnet50模型为基础，任务还是10分类任务。不同点在于，我们希望利用已有的模型结构，在倒数第二层增加一个额外的输入变量add_variable来辅助预测。具体实现如下：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">net</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">Model</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="n">net</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fc_add</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">1001</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Softmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        
    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">add_variable</span><span class="p">):</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">x</span><span class="p">)),</span> <span class="n">add_variable</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)),</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc_add</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">x</span>
</pre></div>
</div>
<p>这里的实现要点是通过torch.cat实现了tensor的拼接。torchvision中的resnet50输出是一个1000维的tensor，我们通过修改forward函数（配套定义一些层），先将1000维的tensor通过激活函数层和dropout层，再和外部输入变量&quot;add_variable&quot;拼接，最后通过全连接层映射到指定的输出维度10。</p>
<p>另外这里对外部输入变量&quot;add_variable&quot;进行unsqueeze操作是为了和net输出的tensor保持维度一致，常用于add_variable是单一数值 (scalar) 的情况，此时add_variable的维度是 (batch_size, )，需要在第二维补充维数1，从而可以和tensor进行torch.cat操作。对于unsqueeze操作可以复习下2.1节的内容和配套代码。</p>
<p>之后对我们修改好的模型结构进行实例化，就可以使用了：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">net</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet50</span><span class="p">()</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">net</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
</pre></div>
</div>
<p>另外别忘了，训练中在输入数据的时候要给两个inputs：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">add_var</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="id3">
<h2>5.3.3 添加额外输出<a class="headerlink" href="#id3" title="永久链接至标题">#</a></h2>
<p>有时候在模型训练中，除了模型最后的输出外，我们需要输出模型某一中间层的结果，以施加额外的监督，获得更好的中间层结果。基本的思路是修改模型定义中forward函数的return变量。</p>
<p>我们依然以resnet50做10分类任务为例，在已经定义好的模型结构上，同时输出1000维的倒数第二层和10维的最后一层结果。具体实现如下：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">net</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">Model</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="n">net</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Softmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        
    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">add_variable</span><span class="p">):</span>
        <span class="n">x1000</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x10</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">x1000</span><span class="p">))</span>
        <span class="n">x10</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">x10</span><span class="p">)</span>
        <span class="n">x10</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output</span><span class="p">(</span><span class="n">x10</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">x10</span><span class="p">,</span> <span class="n">x1000</span>
</pre></div>
</div>
<p>之后对我们修改好的模型结构进行实例化，就可以使用了：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
<span class="n">net</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet50</span><span class="p">()</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">net</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
</pre></div>
</div>
<p>另外别忘了，训练中在输入数据后会有两个outputs：</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">out10</span><span class="p">,</span> <span class="n">out1000</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">add_var</span><span class="p">)</span>
</pre></div>
</div>
</section>
</section>


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